Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification

In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification. We made an optimization model for geometry-aware discrimination dictionary learning in which the dictionary learning (GADDL) is generalized from Euclidian space to Riemannian manifolds, and dictionary atoms are composed of manifold data. An efficient optimization algorithm based on an alternating direction multiplier method was developed to solve the model. Experiments were implemented on three public datasets: Flevoland-1989, San Francisco and Flevoland-1991. The experimental results show that the proposed method learned a discriminative dictionary with accuracies better those of comparative methods. The convergence of the model and the robustness of the initial dictionary were also verified through experiments.

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Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning

Remote Sensing ◽

10.3390/rs11070769 ◽

2019 ◽

Vol 11 (7) ◽

pp. 769 ◽

Cited By ~ 3

Author(s):

Huiping Lin ◽

Hang Chen ◽

Hongmiao Wang ◽

Junjun Yin ◽

Jian Yang

Keyword(s):

Learning Strategies ◽

Dictionary Learning ◽

Binary Classification ◽

Contextual Information ◽

Polarimetric Synthetic Aperture Radar ◽

Robust Detection ◽

Ship Detection ◽

Sparsity Constraints ◽

Active Learning Strategies ◽

Discriminative Dictionary Learning

Ship detection with polarimetric synthetic aperture radar (PolSAR) has received increasing attention for its wide usage in maritime applications. However, extracting discriminative features to implement ship detection is still a challenging problem. In this paper, we propose a novel ship detection method for PolSAR images via task-driven discriminative dictionary learning (TDDDL). An assumption that ship and clutter information are sparsely coded under two separate dictionaries is made. Contextual information is considered by imposing superpixel-level joint sparsity constraints. In order to amplify the discrimination of the ship and clutter, we impose incoherence constraints between the two sub-dictionaries in the objective of feature coding. The discriminative dictionary is trained jointly with a linear classifier in task-driven dictionary learning (TDDL) framework. Based on the learnt dictionary and classifier, we extract discriminative features by sparse coding, and obtain robust detection results through binary classification. Different from previous methods, our ship detection cue is obtained through active learning strategies rather than artificially designed rules, and thus, is more adaptive, effective and robust. Experiments performed on synthetic images and two RADARSAT-2 images demonstrate that our method outperforms other comparative methods. In addition, the proposed method yields better shape-preserving ability and lower computation cost.

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Hyperspectral Image Classification Using Discriminative Dictionary Learning

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/17/1/012222 ◽

2014 ◽

Vol 17 ◽

pp. 012222 ◽

Cited By ~ 3

Author(s):

Y Zongze ◽

S Hao ◽

J Kefeng ◽

Z Huanxin

Keyword(s):

Image Classification ◽

Dictionary Learning ◽

Hyperspectral Image ◽

Hyperspectral Image Classification ◽

Discriminative Dictionary Learning

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Modified Hybrid Freeman/Eigenvalue Decomposition for Polarimetric SAR Data

Journal of Sensors ◽

10.1155/2015/168720 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Shuang Zhang ◽

Shuang Wang ◽

Bo Chen

Keyword(s):

Synthetic Aperture Radar ◽

San Francisco ◽

Decomposition Method ◽

Unitary Transformation ◽

Synthetic Aperture ◽

Eigenvalue Decomposition ◽

Reflection Symmetry ◽

Polarimetric Sar ◽

Polarimetric Synthetic Aperture Radar ◽

Airborne Sensors

Because of the rapid advancement of the airborne sensors and spaceborne sensors, large volumes of fully polarimetric synthetic aperture radar (PolSAR) data are available, but they are too complex to interpret difficultly. In this paper, a modified hybrid Freeman/eigenvalue decomposition method for the coherency matrix derived from the fully PolSAR sensors is proposed. The proposed modified hybrid Freeman/eigenvalue decomposition uses a real unitary transformation on the coherency matrix to release correlations between the copolarized term and cross polarized term, and the scattering models are derived from eigenvectors of the coherency matrix with reflection symmetry condition. The anisotropy and entropy are used to determine whether the volume scattering component is derived from the man-made structures or not. Moreover, the scattering powers from the proposed hybrid Freeman/eigenvalue decomposition are all nonnegative values. Fully PolSAR data on San Francisco acquired by AIRSAR sensor are used in the experiments to prove the efficacy of the proposed decomposition.

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